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oapen-20.500.12657-230262024-03-22T19:23:36Z Bioimage Data Analysis Workflows Miura, Kota Sladoje, Nataša Medicine Biomedical engineering Cell biology Bioinformatics Biology—Technique Systems biology Biological systems thema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services::MQW Biomedical engineering thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSF Cellular biology (cytology) This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows. The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images. 2020-03-18 13:36:15 2020-04-01T09:00:33Z 2020-04-01T09:00:33Z 2020 book 1007135 http://library.oapen.org/handle/20.500.12657/23026 eng Learning Materials in Biosciences application/pdf n/a 1007135.pdf https://www.springer.com/9783030223861 Springer Nature 10.1007/978-3-030-22386-1 10.1007/978-3-030-22386-1 6c6992af-b843-4f46-859c-f6e9998e40d5 170 Cham open access
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This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows. The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
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